ABSTRACT

The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services have emphasised unequivocal evidences about the impact of human actions on climate and biodiversity at alarming rates. In Brazilian terms, 2019 has been marked by controversial discussions among politicians and environmentalists, leading to misinformation and misinterpretations that clearly motivate the continuous collection and scientific analysis of data to support sustainable solutions. Aiming at dealing with this issue, this manuscript brings two contributions: (i) the creation of the Brazilian Forest Dataset, including Brazilian seed plants, Fraction of Absorbed Photosynthetically Active Radiation, meteorological and geographical data composing 8,482 attributes to model and predict 20 vegetation types; and (ii) the feasibility analysis on modelling this dataset in light of supervised machine learning algorithms, so we devise confident results on the Brazilian biodiversity. Experimental results confirm Random Forest and Support Vector Machines successfully adjust models, enabling researchers to predict the occurrence of specific types of vegetation in different regions of Brazil as well as analyse how the prediction accuracy changes along time after the collection of new data. Our contributions bring important tools to support the study on the evolution of the Brazilian biodiversity.

摘要

政府间气候变化专门委员会和政府间生物多样性和生态系统服务科学政策平台强调了关于人类活动以惊人的速度对气候和生物多样性产生影响的明确证据。用巴西的话说，2019 年政治家和环保主义者之间的讨论充满争议，导致错误信息和误解，这显然促使人们对数据进行持续收集和科学分析，以支持可持续解决方案。为了解决这个问题，本手稿有两个贡献：(i) 巴西森林数据集的创建，包括巴西种子植物、吸收的光合有效辐射的分数、气象和地理数据，由 8,482 个属性组成，用于对 20 种植被类型进行建模和预测; (ii) 根据监督机器学习算法对该数据集进行建模的可行性分析，因此我们对巴西的生物多样性设计了可靠的结果。实验结果证实随机森林和支持向量机成功地调整了模型，使研究人员能够预测巴西不同地区特定类型植被的发生情况，并分析在收集新数据后预测精度如何随时间变化。我们的贡献带来了重要的工具来支持巴西生物多样性演变的研究。使研究人员能够预测巴西不同地区特定类型植被的发生情况，并分析在收集新数据后预测精度如何随时间变化。我们的贡献带来了重要的工具来支持巴西生物多样性演变的研究。使研究人员能够预测巴西不同地区特定类型植被的发生情况，并分析在收集新数据后预测精度如何随时间变化。我们的贡献带来了重要的工具来支持巴西生物多样性演变的研究。